# Quickstart

### Create a Dataset

The first step is to [create a dataset](https://app.anndb.com/datasets/new) for your data. AnnDB provides multiple dataset types which provide out-of-the-box solutions for image similarity search, text semantic search, question answering, or raw approximate nearest neighbours search in case you want to use your own vector embeddings.

![](/files/-MYkEgiQwI-yWuBWlUbd)

### Create an API Key

Next, you will need to [create an API key](https://app.anndb.com/api_keys/new) to authenticate your client application with the AnnDB API. You can either a universal API key for all datasets, or you can create a dataset-specific API key to limit the access scope.

![](/files/-MYjygWgNhyd6F-beWdV)

### Install AnnDB

AnnDB provides client implementations in the following languages: [Python](https://github.com/anndb-com/anndb-api-client-python), [Ruby](https://github.com/anndb-com/anndb-api-client-ruby). Clients allow you to modify and search the data stored in your datasets. 

{% tabs %}
{% tab title="Python" %}

```
pip install anndb-api
```

{% endtab %}

{% tab title="Ruby" %}

```
gem install anndb_api
```

{% endtab %}
{% endtabs %}

### Hello, world!

This example application shows, how easy it is to build an image similarity search service with AnnDB in just a few lines of code.

{% tabs %}
{% tab title="Python" %}

```python
import anndb_api

# Create a client instance
client = anndb_api.Client('<YOUR_API_KEY')

# Load the dataset
dataset = client.images('<DATASET_NAME>')

# Insert the data
for url in img_urls:
    id = dataset.insert(url, metadata={'src_url': url})
    
# Delete some of it
dataset.delete(id)

# Query top 5 similar images
items = dataset.search_image(img_urls[-1], 5)

# Query top 5 similar images using textual query
items = dataset.search_text('cute puppy', 5)
```

{% endtab %}

{% tab title="Ruby" %}

```ruby
require 'anndb_api'

# Create a client instance
client = AnndbApi::Client.new("<YOUR_API_KEY")

# Load the dataset
dataset = client.images("<DATASET_NAME>")

img_urls.each do |url|
    id = dataset.insert(url, metadata={ "src_url": url })
end
    
# Delete some of it
dataset.delete(id)

# Query top 5 similar images
items = dataset.search_image(img_urls.last, 5)

# Query top 5 similar images using textual query
items = dataset.search_text("cute puppy", 5)
```

{% endtab %}
{% endtabs %}


---

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